Today, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.
Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks below.
First, start with installing the relevant packages ‘tidyverse’, ‘gganimate’, and ‘gapminder’.
## Warning: package 'tidyverse' was built under R version 4.0.5
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.4 v dplyr 1.0.7
## v tidyr 1.1.4 v stringr 1.4.0
## v readr 2.0.2 v forcats 0.5.1
## Warning: package 'ggplot2' was built under R version 4.0.5
## Warning: package 'tibble' was built under R version 4.0.5
## Warning: package 'tidyr' was built under R version 4.0.5
## Warning: package 'readr' was built under R version 4.0.5
## Warning: package 'dplyr' was built under R version 4.0.5
## Warning: package 'forcats' was built under R version 4.0.5
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## Warning: package 'gganimate' was built under R version 4.0.5
## Warning: package 'gifski' was built under R version 4.0.5
## Warning: package 'av' was built under R version 4.0.5
## Warning: package 'gapminder' was built under R version 4.0.5
First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.
str(gapminder)
## tibble [1,704 x 6] (S3: tbl_df/tbl/data.frame)
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ year : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num [1:1704] 28.8 30.3 32 34 36.1 ...
## $ pop : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num [1:1704] 779 821 853 836 740 ...
unique(gapminder$year)
## [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
head(gapminder)
## # A tibble: 6 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.
Let’s plot all the countries in 1952.
theme_set(theme_bw()) # set theme to white background for better visibility
ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10()
…
We see an interesting spread with an outlier to the right. Answer the following questions, please:
When we have such a big pool of data that ranges over several orders of magnitude on the x-axis it helps to use log10 scale, because it allows a larger range of values to be shown without the smallest becoming pressed down in the bottom.
I am creating an object that contains the variables country and gdpPercap in 1952 arranged in a descending order, so that the richest countries are placed first in the tibble. To get it to print only the country with the highest gdpPercap I subset ‘[]’ the previously made tibble:
gapminder_year_1952 <- gapminder %>%
filter(year == 1952) %>%
select(country, gdpPercap) %>%
arrange(desc(gdpPercap))
gapminder_year_1952[1, 1:2]
## # A tibble: 1 x 2
## country gdpPercap
## <fct> <dbl>
## 1 Kuwait 108382.
Next, you can generate a similar plot for 2007 and compare the differences
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10()
…
The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.
Tasks:
To easily differentiated the continent by colour, I, further specified the ggplot to change the color for the different continents within the aesthetic (aes) function:
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop, color = continent)) +
geom_point() +
scale_x_log10()
To fix the axis labels and units I used the labs function. Within the function I specified it to change the titles of the labels of the x-and y-axis and the size and colour of the font for the different units and labels. Finally, I removed the ‘scientific notation’:
options(scipen = 10)
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop, color = continent)) +
geom_point() +
scale_x_log10() +
labs(x = "gdp per capita", y = "Life expectancy") +
theme(axis.text.x = element_text(colour = "grey20", size = 12), axis.text.y = element_text(colour = "grey20", size = 12), text = element_text(size = 14))
To find the 5 richest countries in 2007 I created an object that contains the data from the variables country and gdpPercap in 2007 arranged in a descending order, so that the richest countries are placed first in the tibble. To get it to only print the 5 richest countries I used the head function on the created object (tibble):
gapminder_2007 <- gapminder %>%
filter(year == 2007) %>%
select(country, gdpPercap) %>%
arrange(desc(gdpPercap))
head(gapminder_2007)
## # A tibble: 6 x 2
## country gdpPercap
## <fct> <dbl>
## 1 Norway 49357.
## 2 Kuwait 47307.
## 3 Singapore 47143.
## 4 United States 42952.
## 5 Ireland 40676.
## 6 Hong Kong, China 39725.
The comparison would be easier if we had the two graphs together, animated. We have a lovely tool in R to do this: the gganimate
package. Beware that there may be other packages your operating system
needs in order to glue interim images into an animation or video. Read
the messages when installing the package.
Also, there are two ways of animating the gapminder ggplot.
The first step is to create the object-to-be-animated
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() # convert x to log scale
anim
…
This plot collates all the points across time. The next step is to
split it into years and animate it. This may take some time, depending
on the processing power of your computer (and other things you are
asking it to do). Beware that the animation might appear in the bottom
right ‘Viewer’ pane, not in this rmd preview. You need to knit the document to get the visual inside an html file.
anim + transition_states(year,
transition_length = 1,
state_length = 1)
…
Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.
This option smoothes the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() + # convert x to log scale
transition_time(year)
anim2
The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.
Now, choose one of the animation options and get it to work. You may need to troubleshoot your installation of gganimate and other packages
transition_states() and transition_time() functions respectively)To add a title that changed in sync with the animation I used the labs function and specified adding a title called year that changes depending on the time frame:
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() +
transition_time(year) +
labs(title = "year: {frame_time} ")
anim2
By running this code, the title (the year) will change in sync with the animation in the left corner of the plot.
The code I have created:
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() +
transition_time(year) +
labs(title = "year: {frame_time} ", x = "gdp per capita", y = "Life expectancy") +
theme(axis.text.x = element_text(colour = "grey20", size = 12), axis.text.y = element_text(colour = "grey20", size = 12), text = element_text(size = 14))
anim2
It produces an animation of a plot where the titles are no longer abbreviated, bigger in font size, slightly different in colour (grey and black) and have no ‘scientific notation’.
gapminder_unfiltered dataset and download more at https://www.gapminder.org/data/ ]